利用隱式型態模式之自適應車行監控畫面分析系統

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：69

、訪客IP：3.12.34.209

姓名

許凱凱(Kai-kai Hsu) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

利用隱式型態模式之自適應車行監控畫面分析系統
(Adaptive Traffic Scene Analysis by using Implicit Shape Model)

相關論文

★ 基於QT之跨平台無線心率分析系統實現	★ 網路電話之額外訊息傳輸機制
★ 針對與運動比賽精彩畫面相關串場效果之偵測	★ 植基於向量量化之視訊/影像內容驗證技術
★ 植基於串場效果偵測與內容分析之棒球比賽精華擷取系統	★ 以視覺特徵擷取為基礎之影像視訊內容認證技術
★ 使用動態背景補償以偵測與追蹤移動監控畫面之前景物	★ 應用於H.264/AVC視訊內容認證之適應式數位浮水印
★ 棒球比賽精華片段擷取分類系統	★ 利用H.264/AVC特徵之多攝影機即時追蹤系統
★ 利用隱式型態模式之高速公路前車偵測機制	★ 基於時間域與空間域特徵擷取之影片複製偵測機制
★ 結合數位浮水印與興趣區域位元率控制之車行視訊編碼	★ 應用於數位智權管理之H.264/AVC視訊加解密暨數位浮水印機制
★ 基於文字與主播偵測之新聞視訊分析系統	★ 植基於數位浮水印之H.264/AVC視訊內容驗證機制

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

本研究提出一個針對固定式道路監視畫面之分析工具，用以協助解決車輛影像交疊問題，並提升車流評估及車輛分類準確度。本論文主要分為兩個部份，第一部份為模型訓練機制，經由搜集之交通場景及車輛相關資訊，分析其統計特性，取得目標道路車流方向及出現之機車、汽車、公車等各類車輛大小資訊，接著以自動化的方式建立交通場景模型及代表車輛之隱式型態模式 (ISM)。值得注意的是，此自適應機制可以大幅減少模型建置的人力需求。第二部份結合了訓練完成的ISM，對可能發生車輛影像交疊的部份進行辨識。實驗結果顯示了這個機制確實能夠適應不同的交通場景，並且有效地解決道路監視器畫面中車輛影像交疊的問題。

摘要(英)

This research presents a framework of analyzing the traffic
information in the surveillance videos from the static roadside cameras to assist resolving the vehicle occlusion problem for more accurate traffic flow estimation and vehicle classification. The proposed scheme consists of two main parts. The first part is a model training mechanism, in which the traffic and vehicle information will be collected and their statistics are employed to automatically establish the model of the scene and the implicit shape model of vehicles. It should be noted that the proposed self-training mechanism can reduce a great deal of human efforts. The second part adopts the established implicit shape model, which is a highly flexible learned representation, for vehicle recognition when possible occlusions of vehicles are detected. Experimental results demonstrate that the proposed scheme can deal with the scenes with different characteristics and the occlusion problem in traffic surveillance videos can be reasonably resolved.

關鍵字(中)

★ 交通監控
★ 車輛
★ 交疊

關鍵字(英)

★ vehicle
★ traffic
★ surveillance
★ occlusion
★ SIFT

論文目次

Contents
1 Introduction 1
1.1 Significance of the Research . . . . . . . . . . . . . . 1
1.2 Contributions of the Research . . . . . . . . . . . . . 2
1.3 The Organization of Thesis . . . . . . . . . . . . . . 3
2 The Related Works 4
2.1 Self-Training Mechanism . . . . . . . . . . . . . . . . 4
2.2 Vehicle Occlusion Handling . . . . . . . . . . . . . . 6
3 The Proposed Self-Training Scheme 9
3.1 System Overview . . . . . . . . . . . . . . . . . . . . 9
3.2 Background Model Construction . . . . . . . . . . . . 11
3.3 Occlusion Vehicle Detection . . . . . . . . . . . . . . 12
3.4 Review of Scale-Invariant Feature Transform . . . . . 13
3.5 Traffic Information Analysis . . . . . . . . . . . . . . 17
3.6 Review of Implicit Shape Model . . . . . . . . . . . . 19
3.6.1 Shape Model Establishment . . . . . . . . . . 19
3.6.2 Recognition Approach . . . . . . . . . . . . . 22
3.7 Vehicle Shape Model Construction . . . . . . . . . . 25
3.8 Occlusion Resolving . . . . . . . . . . . . . . . . . . . 26
4 Experimental Results 30
4.1 Traffic Information Analysis . . . . . . . . . . . . . . 31
4.2 Vehicle Pattern Extraction and Classification . . . . . 32
4.3 Occlusion Resolving . . . . . . . . . . . . . . . . . . . 38
5 Conclusion and Future Work 42
Reference 44

參考文獻

A. Levin, P. Viola, and Y. Freund, ``Unsupervised improvement of visual detectors using co-training," in Proc. ICCV, vol. 2. Citeseer, 2003, pp. 626-633.
V. Nair and J. Clark, ``An unsupervised, online learning framework for moving object detection," Computer Vision and Pattern Recognition, vol. 2, p. 317324, 2004.
O. Javed, S. Ali, and M. Shah, ``Online detection and classification of moving objects using progressively improving detectors," Computer Vision and Pattern Recognition, vol. 1, p. 696701, 2005.
J. Hsieh, S. Yu, Y. Chen, and W. Hu, ``Automatic traffic surveillance system for vehicle tracking and classification," IEEE Transactions on Intelligent Transportation Systems, vol. 7, no. 2, pp. 175-187, 2006.
B. Wu and R. Nevatia, ``Improving part based object detection by unsupervised, online boosting," in IEEE Conference on Computer Vision and Pattern Recognition, 2007. CVPR'07, 2007, pp. 1-8.
J. Zhou, D. Gao, and D. Zhang, ``Moving vehicle detection for automatic traffic monitoring," IEEE transactions on vehicular technology, vol. 56, no. 1, pp. 51-59, 2007.
H. Celik, A. Hanjalic, E. Hendriks, and S. Boughorbel, ``Online training of object detectors from unlabeled surveillance video," in IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008. CVPRW'08, 2008, pp. 1-7.
H. Celik, A. Hanjalic, and E. Hendriks, ``Unsupervised and simultaneous training of multiple object detectors from unlabeled surveillance video," Computer Vision and Image Understanding, vol. 113, no. 10, pp. 1076-1094, 2009.
C. Pang, W. Lam, and N. Yung, ``A novel method for resolving vehicle occlusion in a monocular traffic-image sequence," IEEE Transactions on Intelligent Transportation Systems, vol. 5, pp. 129-141, 2004.
C. Pang, W. Lam, and N. Yung, ``A method for vehicle count in the presence of multiple-vehicle occlusions in traffic images," IEEE Transactions on Intelligent Transportation Systems, vol. 8, no. 3, pp. 441-459, 2007.
X. Song and R. Nevatia, ``A model-based vehicle segmentation method for tracking," in Tenth IEEE International Conference on Computer Vision, 2005. ICCV 2005, 2005, pp. 1124-1131.
C. Gentile, O. Camps, and M. Sznaier, ``Segmentation for robust tracking in the presence of severe occlusion," 2001.
N. Kanhere, S. Birch eld, and W. Sarasua, ``Vehicle segmentation and tracking in the presence of occlusions," Transportation Research Record: Journal of the Transportation Research Board, vol. 1944, no. -1, pp. 89-97, 2006.
L. Tsai, J. Hsieh, and K. Fan, ``Vehicle detection using normalized color and edge map," IEEE Transactions on Image Processing, vol. 16, no. 3, pp. 850-864, 2007.
C.Wang and J. Lien, ``Automatic Vehicle Detection Using Local Features A Statistical Approach," IEEE Transactions on Intelligent Transportation Systems, vol. 9, no. 1, pp. 83-96, 2008.
S. Kamijo, Y. Matsushita, K. Ikeuchi, and M. Sakauchi, ``Traffic monitoring and accident detection at intersections," IEEE transactions on Intelligent transportation systems, vol. 1, no. 2, pp. 108-118, 2000.
C. Huang and W. Liao, ``A Vision-Based Vehicle Identification System," Pattern Recognition, vol. 4, pp. 364-367, 2004.
A. Yoneyama, C. Yeh, and C. Kuo, ``Robust vehicle and traffic information extraction for highway surveillance," EURASIP Journal on Applied Signal Processing, vol. 2005, p. 2321, 2005.
W. Zhang, Q. Wu, X. Yang, and X. Fang, ``Multilevel Framework to Detect and Handle Vehicle Occlusion," IEEE Transactions on Intelligent Transportation Systems, vol. 9, no. 1, pp. 161-174, 2008.
J. Lou, T. Tan, W. Hu, H. Yang, and S. Maybank, ``3-D model-based vehicle tracking," IEEE Transactions on image processing, vol. 14, no. 10, pp. 1561-1569, 2005.
D. Lowe, ``Distinctive image features from scale-invariant keypoints," International journal of computer vision, vol. 60, no. 2, pp. 91-110, 2004.
B. Leibe, A. Leonardis, and B. Schiele, ``Robust object detection with interleaved categorization and segmentation," International Journal of Computer Vision, vol. 77, no. 1, pp. 259-289, 2008.
Y. Cheng, ``Mean shift, mode seeking, and clustering," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 17, no. 8, pp. 790-799, 1995.

指導教授

蘇柏齊(Po-Chyi Su)

審核日期

2010-7-27

推文